1,252 research outputs found
Time in causal structure learning
Get PDFA large body of research has explored how the time between two events affects judgments of causal strength between them. In this article, we extend this work in 4 experiments that explore the role of temporal information in causal structure induction with multiple variables. We distinguish two qualitatively different types of information: The order in which events occur, and the temporal intervals between those events. We focus on one-shot learning in Experiment 1. In Experiment 2, we explore how people integrate evidence from multiple observations of the same causal device. Participants’ judgments are well predicted by a Bayesian model that rules out causal structures that are inconsistent with the observed temporal order, and favors structures that imply similar intervals between causally connected components. In Experiments 3 and 4, we look more closely at participants’ sensitivity to exact event timings. Participants see three events that always occur in the same order, but the variability and correlation between the timings of the events is either more consistent with a chain or a fork structure. We show, for the first time, that even when order cues do not differentiate, people can still make accurate causal structure judgments on the basis of interval variability alone. (PsycInfo Database Record (c) 2020 APA, all rights reserved
Active causal structure learning in continuous time
Get PDFResearch on causal cognition has largely focused on learning and reasoning about contingency data aggregated across discrete observations or experiments. However, this setting represents only the tip of the causal cognition iceberg. A more general problem lurking beneath is that of learning the latent causal structure that connects events and actions as they unfold in continuous time. In this paper, we examine how people actively learn about causal structure in a continuous-time setting, focusing on when and where they intervene and how this shapes their learning. Across two experiments, we find that participants' accuracy depends on both the informativeness and evidential complexity of the data they generate. Moreover, participants' intervention choices strike a balance between maximizing expected information and minimizing inferential complexity. People time and target their interventions to create simple yet informative causal dynamics. We discuss how the continuous-time setting challenges existing computational accounts of active causal learning, and argue that metacognitive awareness of one's inferential limitations plays a critical role for successful learning in the wild
Jere Nash Interview with Neil McMillen (Part 2 of 2)
No full textInterview conducted by author Jere Nash with University of Southern Mississippi history professor Neil R. McMillen in the process of writing Mississippi Politics: The Struggle for Power, 1976-2006. Topics discussed include Aaron Henry; race relations after the civil rights movement; and William Winter
Jere Nash Interview with Neil McMillen (Part 1 of 2)
No full textInterview conducted by author Jere Nash with University of Southern Mississippi history professor Neil R. McMillen in the process of writing Mississippi Politics: The Struggle for Power, 1976-2006. Topics dicussed include race and politics in Mississippi; southern historians including Dewey Grantham, C. Vann Woodward, Numan V. Bartley, John Boles; segregation in Mississippi and resistance to change; genesis of McMillin\u27s book Dark Journey; fifteenth Freedom Summer reunion at Millsaps and Tougaloo; John Ditmer; contributing to A History of Mississippi edited by Richard Aubrey McLemore and reaction by the public and University of Southern Mississippi officials; hiring of African American faculty at USM; M.M. Roberts; and William D. McCain
Facing the Future: the Changing Shape of Academic Skills Support at Bournemouth University
No full textThis paper explores the potential impact of changes to higher education in England on student expectations, engagement, lifestyles and diversity, and outlines implications for the development of digital literacy within academic skills support at Bournemouth University (BU). We will investigate how tackling resource constraints with organisational change can also enable efficient, centralised provision of support materials that utilise networks to overcome the risk of fragmented support for digital literacy. We will also look at how changing delivery modes for support can accommodate changing student lifestyles whilst tackling a weakness of centralised support for digital literacy: that it can become detached from the student’s subject-focused academic practice. Finally we will explore how involving students in developing support can help us to face changes to student expectations and engagement whilst ensuring that materials are authentic and speak to learners in their own voice
Causal induction in time
Get PDFCauses require time to propagate their effects. We can see stars at night because of the light they emitted hundreds of years ago. We can smell the fragrant aroma of baking bread because heat gradually changed the structure of the food, emitting particles that traveled on the breeze. In this thesis, I investigate how people use temporal information to make causal inferences. I propose a rational framework for causal induction based on continuous-time evidence, examine human performance in passive and active continuous-time causal learning tasks, and develop bounded rational accounts that can offer explanations for human causal judgments and intervention strategies.
Chapters 2 and 3 review previous theoretical frameworks on causal induction, and empirical work on the role of time in causal induction, respectively. Chapter 4 develops a rational framework for processing temporal evidence. It provides an explanation for how delays shape human causal induction and accounts for human causal judgments across seven different temporal causal learning tasks. Chapter 5 and Chapter 6 test how people passively or actively learn causal structures based on events unfolding in real time. I found people are capable temporal causal learners who successfully identify structures that involve generative and preventative relationships, as well as acyclic and cyclic connections. Nevertheless, the computational demands of normative learning could easily exceed human capacity. People’s causal judgments align better with an algorithm that approximates the normative solution via a simulation and local summary statistics scheme, suggesting the reliance on structurally local computation and temporally local evidence. People’s intervention decisions align better with a resource-rational model that emphasizes a balance between expected information and expected inferential complexity when choosing interventions. Chapter 7 shows that when given a limited period of observation, people not only focus on existing data, but also consider future possibilities, relying on extrapolated data to make inferences. This demonstrates the unique “continuing” feature of time, and how generalization plays a role in the utilization of temporal information. Chapter 8 synthesizes the findings of this thesis and proposes future research directions of causal learning in temporal contexts
Decompose, deduce, and dispose:A memory-limited metacognitive model of human problem solving
Get PDFMany real-world problems are defined by complex systems of interlocking constraints. How people are able to solve these problems with such limited working memory capacity remains poorly understood. We propose a formal model of human problem-solving under memory constraints that uses metacognitive knowledge of its own memory limits to guide subproblem choice. We compare our model to human gameplay in two experiments using a variant of the classic game Minesweeper. In Experiment 1, we find that participants' accuracy was influenced both by the order of subproblems and their ability to externalize intermediate results, indicative of a memory bottleneck in reasoning. In Experiment 2, we used a mouse-tracking paradigm to assess participants' subproblem choice and time allocation. The model captures key patterns of subproblem ordering, error, and time allocation. Our results point toward memory limits and strategies for navigating those limits as central elements of human problem-solving
Decompose, deduce, and dispose:A memory-limited metacognitive model of human problem solving
Get PDFMany real-world problems are defined by complex systems of interlocking constraints. How people are able to solve these problems with such limited working memory capacity remains poorly understood. We propose a formal model of human problem-solving under memory constraints that uses metacognitive knowledge of its own memory limits to guide subproblem choice. We compare our model to human gameplay in two experiments using a variant of the classic game Minesweeper. In Experiment 1, we find that participants' accuracy was influenced both by the order of subproblems and their ability to externalize intermediate results, indicative of a memory bottleneck in reasoning. In Experiment 2, we used a mouse-tracking paradigm to assess participants' subproblem choice and time allocation. The model captures key patterns of subproblem ordering, error, and time allocation. Our results point toward memory limits and strategies for navigating those limits as central elements of human problem-solving
Root growth of lupins is more sensitive to waterlogging than wheat
No full textIn south-west Australia, winter grown crops such as wheat and lupin often experience transient waterlogging during periods of high rainfall. Wheat is believed to be more tolerant to waterlogging than lupins, but until now no direct comparisons have been made. The effects of waterlogging on root growth and anatomy were compared in wheat (Triticum aestivum L.), narrow-leafed lupin (Lupinus angustifolius L.) and yellow lupin (Lupinus luteus L.) using 1m deep root observation chambers. Seven days of waterlogging stopped root growth in all species, except somenodal root development in wheat. Roots of both lupin species died back progressively from the tips while waterlogged. After draining the chambers, wheat root growth resumed in the apical region at a faster rate than well-drained plants, so that total root length was similar in waterlogged and well-drained plants at the end of the experiment. Root growth in yellow lupin resumed in the basal region, but was insufficient to compensate for root death during waterlogging. Narrow-leafed lupin roots did not recover; they continued to deteriorate. The survival and recovery of roots in response to waterlogging was related to anatomical features that influence internal oxygen deficiency and root hydraulic properties.Helen Bramley, Stephen D. Tyerman, David W. Turner and Neil C. Turne
Maximizing Research Impact Through Institutional and National Open-Access Self-Archiving Mandates
No full textNo research institution can afford all the journals its researchers may need, so all articles are losing research impact (usage and citations). Articles made “Open Access,” (OA) by self-archiving them on the web are cited twice as much, but only 15% of articles are being spontaneously self-archived. The only institutions approaching 100% self-archiving are those that mandate it. Surveys show that 95% of authors will comply with a self-archiving mandate; the actual expe-rience of institutions with mandates has confirmed this. What institutions and funders need to mandate is that (1) immediately upon acceptance for publication, (2) the author’s final draft must be (3) deposited into the Institutional Repository. Only the depositing needs to be mandated; set-ting access privileges to the full-text as either OA or Restricted Access (RA) can be left up to the author. For articles published in the 93% of journals that have already endorsed self-archiving, access can be set as OA immediately; for the remaining 7%, authors can email the eprint in re-sponse to individual email requests automatically forwarded by the Repository
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